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Nuclear Medicine and Molecular Imaging in Cancer Diagnosis, Therapy, and...
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Nuclear Medicine and Molecular Imaging in Cancer Diagnosis, Therapy, and Treatment Assessment

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (30 October 2025) | Viewed by 4355

Special Issue Editor

Dr. Sara Harsini

E-Mail Website
Guest Editor
BC Cancer Research Institute, Vancouver, BC, Canada
Interests: medical imaging; molecular imaging; PET/CT; theranostics; cancer imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nuclear medicine and molecular imaging have transformed cancer management by providing unparalleled insights into tumor biology. These modalities are integral not only for early detection and precise staging but also for guiding targeted therapy, monitoring disease progression, predicting prognosis, and assessing minimal residual disease. The integration of advanced imaging technologies such as PET, SPECT, PET/CT, and PET/MRI, along with novel radiopharmaceuticals and theranostic agents, has significantly improved diagnostic accuracy and personalized treatment strategies. Furthermore, innovations in artificial intelligence, radiomics, quantitative imaging biomarkers, and multi-parametric imaging are enhancing precision in oncologic decision-making. This Special Issue invites contributions on a broad spectrum of topics, including novel radiotracers, AI-driven image analysis, multi-modal imaging applications, and the evolving role of nuclear medicine in precision oncology. We welcome original research, reviews, and clinical studies that contribute to the advancement of molecular imaging in cancer diagnosis, treatment planning, and therapeutic assessment.

Dr. Sara Harsini
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • nuclear medicine
  • molecular imaging
  • PET/CT
  • theranostics
  • cancer diagnosis
  • treatment monitoring

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Published Papers (3 papers)

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Research

15 pages, 1523 KB  
Article
Dynamic Whole-Body FDG PET/CT for Predicting Malignancy in Head and Neck Tumors and Cervical Lymphadenopathy
by Gregor Horňák, André H. Dias, Ole L. Munk, Lars C. Gormsen, Jaroslav Ptáček and Pavel Karhan
Diagnostics 2025, 15(20), 2651; https://doi.org/10.3390/diagnostics15202651 - 21 Oct 2025
Viewed by 722
Abstract
Background: Dynamic whole-body (D-WB) FDG PET/CT is a novel technique that enables the direct reconstruction of multiparametric images representing the FDG metabolic uptake rate (MRFDG) and “free” FDG (DVFDG). Applying complementary parameters with distinct characteristics compared to static SUV [...] Read more.
Background: Dynamic whole-body (D-WB) FDG PET/CT is a novel technique that enables the direct reconstruction of multiparametric images representing the FDG metabolic uptake rate (MRFDG) and “free” FDG (DVFDG). Applying complementary parameters with distinct characteristics compared to static SUV images, the aims of this study are as follows: (1) to determine the threshold values of SUV, MRFDG, and DVFDG for malignant and benign lesions; (2) to compare the specificity of MRFDG and DVFDG images with static SUVbw images; and (3) to assess whether any of the dynamic imaging parameters correlate more significantly with malignancy or non-malignancy in the examined lesions based on the measured values obtained from D-WB FDG PET/CT. Methods: The study was a retrospective analysis of D-WB PET/CT data from 43 patients (23 males and 20 females) included both in the context of primary staging as well as imaging performed due to suspicion of post-therapeutic relapse or recurrence. Standard scanning was performed using a multiparametric PET acquisition protocol on a Siemens Biograph Vision 600 PET/CT scanner. Pathological findings were manually delineated, and values for SUVbw, MRFDG, and DVFDG were extracted. The findings were classified and statistically evaluated based on their was histological verification of a malignant or benign lesion. Multinomial and binomial logistic regression analyses were used to find parameters for data classification in different models, employing various combinations of the input data (SUVbw, MRFDG, DVFDG). ROC curves were generated by changing the threshold p-value in the regression models to compare the models and determine the optimal thresholds. Results: Patlak PET parameters (MRFDG and DVFDG) combined with mean SUVbw achieved the highest diagnostic accuracy of 0.82 (95% CI 0.75–0.89) for malignancy detection (F1-score = 0.90). Sensitivity reached 0.85 (95% CI 0.77–0.91) and specificity 0.93 (95% CI 0.87–0.98). Classification accuracy in tumors was 0.86 (95% CI 0.78–0.92) and in lymph nodes 0.81 (95% CI 0.73–0.88). Relative contribution analysis showed that DVFDG accounted for up to 65% of the classification weight. ROC analysis demonstrated AUC values above 0.8 for all models, with optimal thresholds achieving sensitivities of around 0.85 and specificities up to 0.93. Thresholds for malignancy detection were, for mean values, SUVbw > 5.8 g/mL, MRFDG > 0.05 µmol/mL/min, DVFDG > 68%, and, for maximal values, SUVbw > 8.7 g/mL, MRFDG > 0.11 µmol/mL/min, DVFDG > 202%. Conclusions: The D-WB [18F]FDG PET/CT images in this study highlight the potential for improved differentiation between malignant and benign lesions compared to conventional SUVbw imaging in patients with locally advanced head and neck cancers presenting with cervical lymphadenopathy and carcinoma of unknown primary origin (CUP). This observation may be particularly relevant in common diagnostic dilemmas, especially in distinguishing residual or recurrent tumors from post-radiotherapy changes. Further validation in larger cohorts with histopathological confirmation is warranted, as the small sample size in this study may limit the generalizability of the findings. Full article
(This article belongs to the Special Issue Nuclear Medicine and Molecular Imaging in Cancer Diagnosis, Therapy, and Treatment Assessment)
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14 pages, 1632 KB  
Article
Optimizing Attenuation Correction in 68Ga-PSMA PET Imaging Using Deep Learning and Artifact-Free Dataset Refinement
by Masoumeh Dorri Giv, Guluzar Ozbolat, Hossein Arabi, Somayeh Malmir, Shahrokh Naseri, Vahid Roshan Ravan, Hossein Akbari-Lalimi, Raheleh Tabari Juybari, Ghasem Ali Divband, Nasrin Raeisi, Vahid Reza Dabbagh Kakhki, Emran Askari and Sara Harsini
Diagnostics 2025, 15(11), 1400; https://doi.org/10.3390/diagnostics15111400 - 31 May 2025
Viewed by 1603
Abstract
Background/Objectives: Attenuation correction (AC) is essential for achieving quantitatively accurate PET imaging. In 68Ga-PSMA PET, however, artifacts such as respiratory motion, halo effects, and truncation errors in CT-based AC (CT-AC) images compromise image quality and impair model training for deep learning-based AC. [...] Read more.
Background/Objectives: Attenuation correction (AC) is essential for achieving quantitatively accurate PET imaging. In 68Ga-PSMA PET, however, artifacts such as respiratory motion, halo effects, and truncation errors in CT-based AC (CT-AC) images compromise image quality and impair model training for deep learning-based AC. This study proposes a novel artifact-refinement framework that filters out corrupted PET-CT images to create a clean dataset for training an image-domain AC model, eliminating the need for anatomical reference scans. Methods: A residual neural network (ResNet) was trained using paired PET non-AC and PET CT-AC images from a dataset of 828 whole-body 68Ga-PSMA PET-CT scans. An initial model was trained using all data and employed to identify artifact-affected samples via voxel-level error metrics. These outliers were excluded, and the refined dataset was used to retrain the model with an L2 loss function. Performance was evaluated using metrics including mean error (ME), mean absolute error (MAE), relative error (RE%), RMSE, and SSIM on both internal and external test datasets. Results: The model trained with the artifact-free dataset demonstrated significantly improved performance: ME = −0.009 ± 0.43 SUV, MAE = 0.09 ± 0.41 SUV, and SSIM = 0.96 ± 0.03. Compared to the model trained on unfiltered data, the purified data model showed enhanced quantitative accuracy and robustness in external validation. Conclusions: The proposed data purification framework significantly enhances the performance of deep learning-based AC for 68Ga-PSMA PET by mitigating artifact-induced errors. This approach facilitates reliable PET imaging in the absence of anatomical references, advancing clinical applicability and image fidelity. Full article
(This article belongs to the Special Issue Nuclear Medicine and Molecular Imaging in Cancer Diagnosis, Therapy, and Treatment Assessment)
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18 pages, 2660 KB  
Article
The Biochemical–Imaging Connection: Urinary Noradrenaline and Fluorodeoxyglucose-Positron Emission Tomography in Unresectable or Metastatic Pheochromocytomas and Paragangliomas
by Junki Takenaka, Shiro Watanabe, Takashige Abe, Satoshi Takeuchi, Kenji Hirata, Rina Kimura, Hiroshi Ishii, Naoto Wakabayashi, Mungunkhuyag Majigsuren and Kohsuke Kudo
Diagnostics 2025, 15(11), 1305; https://doi.org/10.3390/diagnostics15111305 - 22 May 2025
Viewed by 1552
Abstract
Background/Objectives: Pheochromocytomas and paragangliomas (PPGLs) are rare tumors of neural crest origin that secrete varying levels of catecholamines. [18F]Fluorodeoxyglucose-positron emission tomography (FDG-PET) is a valuable tool for the detection of metastases and the prediction of prognoses. However, varying FDG avidities [...] Read more.
Background/Objectives: Pheochromocytomas and paragangliomas (PPGLs) are rare tumors of neural crest origin that secrete varying levels of catecholamines. [18F]Fluorodeoxyglucose-positron emission tomography (FDG-PET) is a valuable tool for the detection of metastases and the prediction of prognoses. However, varying FDG avidities in PPGLs raise concerns regarding cost-effectiveness and unnecessary radiation exposure. Catecholamine secretion patterns are associated with metastasis and clinical outcomes. This study aimed to explore the relationships among FDG avidity, catecholamine levels, and clinical factors in patients with PPGLs. Methods: This retrospective study included 25 patients with unresectable or metastatic PPGLs scheduled for [131I]metaiodobenzylguanidine therapy with FDG-PET data available within 40 days of urine catecholamine measurements. FDG avidity was assessed using semiquantitative parameters such as the maximum standardized uptake value (SUVmax), total metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Urine catecholamine levels were quantified. Logistic regression and Spearman’s correlation were performed to evaluate the relationship between FDG parameters and urinary catecholamine levels. Results: Urinary noradrenaline levels were significantly higher in patients with FDG-avid lesions than in those without (726.25 μg/day vs. 166.3 μg/day, p = 0.001). Noradrenaline levels showed significant positive correlations with SUVmax, MTV, and TLG (ρ = 0.527, 0.541, and 0.557, respectively; all p < 0.01). Urinary noradrenaline levels predicted FDG avidity with an AUC of 0.849; a cutoff value of 647.5 μg/day achieved 55.6% sensitivity and 100% specificity. Conclusions: Urinary noradrenaline levels were significantly associated with FDG avidity in PPGLs, suggesting their potential utility in predicting FDG-PET outcomes. Therefore, FDG-PET may be unnecessary in PPGL patients with low urinary noradrenaline levels. These findings may help optimize imaging strategies for patients with PPGLs. Full article
(This article belongs to the Special Issue Nuclear Medicine and Molecular Imaging in Cancer Diagnosis, Therapy, and Treatment Assessment)
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